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Expertise is a loosely defined concept that is hard to formalize. Muchresearch has focused on designing efficient algorithms for expert finding inlarge databases in various application domains. The evaluation of suchrecommender systems lies most of the time on human-annotated sets of expertsassociated with topics. The protocol of evaluation consists in using thenamings or short descriptions of these topics as raw queries in order to rankthe available set of candidates. Several measures taken from the field ofinformation retrieval are then applied to rate the rankings of candidatesagainst the ground truth set of experts. In this paper, we apply thistopic-query evaluation methodology with the AMiner data and explore a newdocument-query methodology to evaluate experts retrieval from a set of queriessampled directly from the experts documents. Specifically, we describe twodatasets extracted from AMiner, three baseline algorithms from the literaturebased on several document representations and provide experiment results toshow that using a wide range of more realistic queries provides differentevaluation results to the usual topic-queries.

Impact of the Query Set on the Evaluation of Expert Finding Systems